Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia

Endalie, Demeke; Haile, Getamesay; Taye, Wondmagegn

doi:10.2166/ws.2021.391

Cited by 42 publications

(14 citation statements)

References 32 publications

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“…CART is based on binary splitting and is used for regression and classification problems. The decision tree may handle classification and regression problems (Endalie et al 2021). During the modeling training phase, the data series are segmented into homogenous groups to predict or control an objective variable, ending in the tree regression structure of the model.…”

Section: Preparation Of Data Set For Development Of the Proposed Methodsmentioning

confidence: 99%

Estimating the permeability coefficient of soil using CART and GMDH approaches

2022

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Permeability coefficient of soil (k) is one of the most important parameters in groundwater studies. This study, two robust explicit data-driven methods, including Classification and Regression Trees (CART) and the Group Method of Data Handling (GMDH) were developed using characteristics of soil, i.e., clay content (CC), water content (ω), liquid limit (LL), plastic limit (PL), specific density (γ), void ratio (e) to generate predictive equations for prediction of k. When compared to CART; Mean Absolute Error (MAE) = 0.0051, Root Mean Square Error (RMSE) = 0.0088, Scatter Index (SI) = 64.00%, Correlation Coefficient (R) = 0.7841, Index agreement (Ia) = 0.8830; the GMDH equation produced the lowest error values; MAE = 0.0044, RMSE = 0.0072, SI = 52.17%, R = 0.8493, Ia = 0.9184; in testing stage. Although, GMDH had better performance, however, CART and GMDH could be considered effective approaches for the prediction of k.

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Section: Preparation Of Data Set For Development Of the Proposed Methodsmentioning

confidence: 99%

Estimating the permeability coefficient of soil using CART and GMDH approaches

2022

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“…Endalie et al [13] implemented a rainfall prediction method to Jimma, an area placed from southwestern Oromia, Ethiopia. It has presented the long short term memory (LSTM) based forecast technique able of predicting Jimma's daily rainfalls.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

Alamgeer¹,

Al-Rasheed²,

Alhindi³

et al. 2023

Computers, Materials &Amp; Continua

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Data mining process involves a number of steps from data collection to visualization to identify useful data from massive data set. the same time, the recent advances of machine learning (ML) and deep learning (DL) models can be utilized for effectual rainfall prediction. With this motivation, this article develops a novel comprehensive oppositional moth flame optimization with deep learning for rainfall prediction (COMFO-DLRP) Technique. The proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental changes. Primarily, data pre-processing and correlation matrix (CM) based feature selection processes are carried out. In addition, deep belief network (DBN) model is applied for the effective prediction of rainfall data. Moreover, COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning (COBL) with traditional MFO algorithm. Finally, the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN model. For demonstrating the improved outcomes of the COMFO-DLRP approach, a sequence of simulations were carried out and the outcomes are assessed under distinct measures. The simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

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“…Flood forecasting and rainfall prediction techniques have improved in the last few years due to the need to address immense economic and environmental losses caused by flood. Thirty-nine (39) out of forty-nine (49) articles explored application of deep learning techniques to flood forecasting, rainfall prediction and adopted various hydrologic modelling approaches like rainfall prediction (Yeditha et al, 2021;Chhetri et al, 2020;Endalie et al, 2021;Kumar et al, 2019), streamflow forecasting (Abbas et al, 2020;Kumar et al, 2004;Le et al, 2019;Loganathan & Mahindrakar, 2021), flood hazard and severity assessment (Kanth et al, 2022a;Kaur et al, 2021;Khosravi et al, 2020), rainfall-runoff modelling (Van et al, 2020) and flood susceptibility mapping (Bui et al, 2020). Interestingly, this is an indication that developing countries exhibit high flood vulnerability than developed countries, which have embraced better flood protection infrastructure, AI-informed water dynamics modelling, nature-based ecological solutions, efficient early warning systems, sustainable ecosystem services, sustainable urban design systems, and policies targeted at improving river health and monitoring.…”

Section: Deep Learning Application To Flood Forecasting and Rainfall ...mentioning

confidence: 99%

Review article: Current State of Deep Learning Application to Water-related Disaster Management in Developing Countries

Kareem

Seong

Bastola

et al. 2022

Preprint

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Abstract. Availability of abundant water resources data is a great concern hindering adoption of deep learning techniques (DL) for disaster mitigation in developing countries. However, over the last three decades, a sizeable amount of DL publication in disaster management emanated mostly from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster problems in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 1993–2022 using Web of Science, Scopus, VOSviewer software and PRISMA model schema. Results revealed a ‘slightly’ increasing trend of DL-based water disaster publication in developing countries (tau = 0.35, p = 0.00045, Sen-slope, s = 0.00 at confidence level of 95 %), as opposed to the ‘significantly’ increasing trend globally (tau = 0.910, p = 1.72 e-12, Sen-slope, s = 2.52 at confidence level of 95 %). Also, pluvio-fluvial flooding is found to constitute 78 % most disaster prevalence and China is the only ‘high human development’ developing country with an impressive 51 % DL adoption rate, due to China’s increasing need for AI-based solutions to persistent multiyear severe water stress, climate change, environmental degradation, recurrent flood, and saltwater intrusion into estuaries. COVID-19 among other factors is identified as a driver of DL adoption. Further analysis indicates that developing countries will experience implementation delay based on their low Human Development Indices (HDI) because model deployment in solving disaster problems in real life scenarios is currently lacking due to high cost. Therefore, data augmentation, transfer learning, intensive research, deployment using cheap web-based servers and APIs are recommended to enhance disaster preparedness. Developing countries can explore these solutions to foster inclusion in global DL-based disaster mitigation approaches.

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Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia

Cited by 42 publications

References 32 publications

Estimating the permeability coefficient of soil using CART and GMDH approaches

Estimating the permeability coefficient of soil using CART and GMDH approaches

Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

Review article: Current State of Deep Learning Application to Water-related Disaster Management in Developing Countries

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